// One-way ANOVA in R - ALL IN ONE (Calculation, Interpretation, Reporting) //
This video will help you in conducting a one-way ANOVA in R, including the calculation of post-hoc-tests, the effect size as well as interpreting and reporting its results.
Please don't forget that an a priori sample size calculation is usually required.
Calculating the required sample size:
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🎥 [ Ссылка ]
The video consists of the following five parts:
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1) Calculation of the one-way ANOVA in R using the anova_test()-function.
2) Conducting post-hoc-tests to see which pairwise comparisons show differences worth investigating further (t-tests are being used).
3) Interpretation of the results, especially the post-hoc-tests.
4) Calculation of the effect size for the post-hoc-tests of the one-way ANOVA, namely the effect size d. (Effect size Eta² for the one-way ANOVA is shown here: [ Ссылка ])
5) Reporting of the results. Be aware that research field-specific standards may apply. The reporting shown is usually sufficient.
General information on the one-way ANOVA
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The one-way ANOVA (also one-factorial ANOVA) is used to assess whether the mean of at least three groups is different. You have to use a dependent variable that is on the interval or ratio scale.
📚 Sources:
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- Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19-24.
- Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133.
⏰ Timestamps:
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0:00 Introduction
0:15 0. Example
0:34 I. Requirements for the one-way ANOVA
0:42 II. Calculation and interpretation of the one-way ANOVA in R
1:57 III. Post-Hoc-Testing for the the one-way ANOVA in R
4:16 IV. Effect size for post-hoc-tests in R
5:25 V. Reporting the results
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